Topographic time-frequency decomposition of the EEG

Frequency-transformed EEG resting data has been widely used to describe normal and abnormal brain functional states as function of the spectral power in different frequency bands. This has yielded a series of clinically relevant findings. However, by transforming the EEG into the frequency domain, the initially excellent time resolution of time-domain EEG is lost. The topographic time-frequency decomposition is a novel computerized EEG analysis method that combines previously available techniques from time-domain spatial EEG analysis and time-frequency decomposition of single-channel time series. It yields a new, physiologically and statistically plausible topographic time-frequency representation of human multichannel EEG. The original EEG is accounted by the coefficients of a large set of user defined EEG like time-series, which are optimized for maximal spatial smoothness and minimal norm. These coefficients are then reduced to a small number of model scalp field configurations, which vary in intensity as a function of time and frequency. The result is thus a small number of EEG field configurations, each with a corresponding time-frequency (Wigner) plot. The method has several advantages: It does not assume that the data is composed of orthogonal elements, it does not assume stationarity, it produces topographical maps and it allows to include user-defined, specific EEG elements, such as spike and wave patterns. After a formal introduction of the method, several examples are given, which include artificial data and multichannel EEG during different physiological and pathological conditions.

Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self.

Multichannel EEG of an advanced meditator was recorded during four different, repeated meditations. Locations of intracerebral source gravity centers as well as Low Resolution Electromagnetic Tomography (LORETA) functional images of the EEG 'gamma' (35-44 Hz) frequency band activity differed significantly between meditations. Thus, during volitionally self-initiated, altered states of consciousness that were associated with different subjective meditation states, different brain neuronal populations were active. The brain areas predominantly involved during the self-induced meditation states aiming at visualization (right posterior) and verbalization (left central) agreed with known brain functional neuroanatomy. The brain areas involved in the self-induced, meditational dissolution and reconstitution of the experience of the self (right fronto-temporal) are discussed in the context of neural substrates implicated in normal self-representation and reality testing, as well as in depersonalization disorders and detachment from self after brain lesions.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Association of individual resting state EEG alpha frequency and cerebral blood flow

Cognitive task performance differs considerably between individuals. Besides cognitive capacities, attention might be a source of such differences. The individual's EEG alpha frequency (IAF) is a putative marker of the subject's state of arousal and attention, and was found to be associated with task performance and cognitive capacities. However, little is known about the metabolic substrate (i.e. the network) underlying IAF. Here we aimed to identify this network. Correlation of IAF with regional Cerebral Blood Flow (rCBF) in fifteen young healthy subjects revealed a network of brain areas that are associated with the modulation of attention and preparedness for external input, which are relevant for task execution. We hypothesize that subjects with higher IAF have pre-activated task-relevant networks and thus are both more efficient in the task-execution, and show a reduced fMRI-BOLD response to the stimulus, not because the absolute amount of activation is smaller, but because the additional activation by processing of external input is limited due to the higher baseline.

Peri-ictal correlation dynamics of high-frequency (80-200 Hz) intracranial EEG

To assess (1) how large-scale correlation of intracranial EEG signals in the high-frequency range (80-200Hz) evolves from the pre-ictal, through the ictal into the postictal state and (2) whether the contribution of local neuronal activity to large-scale EEG correlation differentiates epileptogenic from non-epileptogenic brain tissue.

EEG correlation and power during maintenance of wakefulness test after sleep-deprivation

To investigate whether there are any objective EEG characteristics that change significantly between specific time periods during maintenance of wakefulness test (MWT) and whether such changes are associated with the ability to appropriately communicate sleepiness.

Personal neglect-a disorder of body representation?

The cognitive mechanisms underlying personal neglect are not well known. One theory postulates that personal neglect is due to a disorder of contralesional body representation. In the present study, we have investigated whether personal neglect is best explained by impairments in the representation of the contralesional side of the body, in particular, or a dysfunction of the mental representation of the contralesional space in general. For this, 22 patients with right hemisphere cerebral lesions (7 with personal neglect, 15 without personal neglect) and 13 healthy controls have been studied using two experimental tasks measuring representation of the body and extrapersonal space. In the tasks, photographs of left and right hands as well as left and right rear-view mirrors presented from the front and the back had to be judged as left or right. Our results show that patients with personal neglect made more errors when asked to judge stimuli of left hands and left rear-view mirrors than either patients without personal neglect or healthy controls. Furthermore, regression analyses indicated that errors in interpreting left hands were the best predictor of personal neglect, while other variables such as extrapersonal neglect, somatosensory or motor impairments, or deficits in left extrapersonal space representation had no predictive value of personal neglect. These findings suggest that deficient body representation is the major mechanism underlying personal neglect.

Forbidden ordinal patterns of periictal intracranial EEG indicate deterministic dynamics in human epileptic seizures

Epileptic seizures typically reveal a high degree of stereotypy, that is, for an individual patient they are characterized by an ordered and predictable sequence of symptoms and signs with typically little variability. Stereotypy implies that ictal neuronal dynamics might have deterministic characteristics, presumably most pronounced in the ictogenic parts of the brain, which may provide diagnostically and therapeutically important information. Therefore the goal of our study was to search for indications of determinism in periictal intracranial electroencephalography (EEG) studies recorded from patients with pharmacoresistent epilepsy.